Python常见的pandas用法demo示例

本文实例总结了Python常见的pandas用法。分享给大家供大家参考，具体如下：

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import numpy as np
import pandas as pd

s = pd.Series([1,3,6, np.nan, 44, 1]) #定义一个序列。 序列就是一列内容，每一行有一个index值
print(s)
print(s.index)

0     1.0
 1     3.0
 2     6.0
 3     NaN
 4    44.0
 5     1.0
 dtype: float64
 RangeIndex(start=0, stop=6, step=1)

dates = pd.date_range('20180101', periods=6)
print(dates)

DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-06'], dtype='datetime64[ns]', freq='D')

df1 = pd.DataFrame(np.arange(12).reshape(3,4)) #定义DataFrame，可以看作一个有index和colunms的矩阵

print(df)

0 1 2 3 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11

df2 = pd.DataFrame(np.random.randn(6,4), index=dates, columns=['a', 'b', 'c', 'd']) #np.random.randn(6,4)生成6行4列矩阵

print(df)

a b c d 2018-01-01 0.300675 1.769383 1.244406 -1.058294 2018-01-02 0.832666 2.216755 0.178716 -0.156828 2018-01-03 1.314190 -0.866199 0.836150 1.001026 2018-01-04 -1.671724 1.147406 -0.148676 -0.272555 2018-01-05 1.146664 2.022861 -1.833995 -0.627568 2018-01-06 -0.192242 1.517676 0.756707 0.058869

df = pd.DataFrame({'A':1.0,

'B':pd.Timestamp('20180101'),

'C':pd.Series(1, index=list(range(4)), dtype='float32'),

'D':np.array([3] * 4, dtype='int32'),

'E':pd.Categorical(['test', 'train', 'test', 'train']),

'F':'foo'}) #按照给出的逐列定义df

print(df)

print(df.dtypes)

A B C D E F 0 1.0 2018-01-01 1.0 3 test foo 1 1.0 2018-01-01 1.0 3 train foo 2 1.0 2018-01-01 1.0 3 test foo 3 1.0 2018-01-01 1.0 3 train foo A float64 B datetime64[ns] C float32 D int32 E category F object dtype: object

#df的行、列、值

print(df.index)

print(df.columns)

print(df.values)

Int64Index([0, 1, 2, 3], dtype='int64') Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object') [[1.0 Timestamp('2018-01-01 00:00:00') 1.0 3 'test' 'foo'] [1.0 Timestamp('2018-01-01 00:00:00') 1.0 3 'train' 'foo'] [1.0 Timestamp('2018-01-01 00:00:00') 1.0 3 'test' 'foo'] [1.0 Timestamp('2018-01-01 00:00:00') 1.0 3 'train' 'foo']]

print(df.describe()) #统计

print(df.T) #转置

A C D count 4.0 4.0 4.0 mean 1.0 1.0 3.0 std 0.0 0.0 0.0 min 1.0 1.0 3.0 25% 1.0 1.0 3.0 50% 1.0 1.0 3.0 75% 1.0 1.0 3.0 max 1.0 1.0 3.0 0 1 2 \ A 1 1 1 B 2018-01-01 00:00:00 2018-01-01 00:00:00 2018-01-01 00:00:00 C 1 1 1 D 3 3 3 E test train test F foo foo foo 3 A 1 B 2018-01-01 00:00:00 C 1 D 3 E train F foo

#df排序

print(df.sort_index(axis=1, ascending=False)) #根据索引值对各行进行排序（相当于重新排列各列的位置）

print(df.sort_values(by='E')) #根据内容值对各列进行排序

F E D C B A 0 foo test 3 1.0 2018-01-01 1.0 1 foo train 3 1.0 2018-01-01 1.0 2 foo test 3 1.0 2018-01-01 1.0 3 foo train 3 1.0 2018-01-01 1.0 A B C D E F 0 1.0 2018-01-01 1.0 3 test foo 2 1.0 2018-01-01 1.0 3 test foo 1 1.0 2018-01-01 1.0 3 train foo 3 1.0 2018-01-01 1.0 3 train foo

indexes = pd.date_range('20180101', periods=6)

df3 = pd.DataFrame(np.arange(24).reshape(6, 4), index=indexes, columns=['A', 'B', 'C', 'D'])

print(df3)

print()

#选择column

print(df3['A'])

print()

print(df3.A)

A B C D 2018-01-01 0 1 2 3 2018-01-02 4 5 6 7 2018-01-03 8 9 10 11 2018-01-04 12 13 14 15 2018-01-05 16 17 18 19 2018-01-06 20 21 22 23 2018-01-01 0 2018-01-02 4 2018-01-03 8 2018-01-04 12 2018-01-05 16 2018-01-06 20 Freq: D, Name: A, dtype: int32 2018-01-01 0 2018-01-02 4 2018-01-03 8 2018-01-04 12 2018-01-05 16 2018-01-06 20 Freq: D, Name: A, dtype: int32 A B C D 2018-01-01 0 1 2 3 2018-01-02 4 5 6 7 2018-01-03 8 9 10 11

#选择行，类似limit语句

print(df3[0:0])

print()

print(df3[0:3])

print()

print(df3['20180103':'20180105'])

Empty DataFrame Columns: [A, B, C, D] Index: [] A B C D 2018-01-01 0 1 2 3 2018-01-02 4 5 6 7 2018-01-03 8 9 10 11 A B C D 2018-01-03 8 9 10 11 2018-01-04 12 13 14 15 2018-01-05 16 17 18 19

print(df3.loc['20180102']) #返回指定行构成的序列

A 4 B 5 C 6 D 7 Name: 2018-01-02 00:00:00, dtype: int32

print(df3.loc['20180103', ['A','C']]) #列筛选

print()

print(df3.loc['20180103':'20180105', ['A','C']]) #子df，类似select A, C from df limit ...

print()

print(df3.loc[:, ['A', 'B']])

A 8 C 10 Name: 2018-01-03 00:00:00, dtype: int32 A C 2018-01-03 8 10 2018-01-04 12 14 2018-01-05 16 18 A B 2018-01-01 0 1 2018-01-02 4 5 2018-01-03 8 9 2018-01-04 12 13 2018-01-05 16 17 2018-01-06 20 21

print(df3);print()

print(df3.iloc[1]);print()

print(df3.iloc[1,1]);print()

print(df3.iloc[:,1]);print()

print(df3.iloc[0:3,1:3]);print()

print(df3.iloc[[1,3,5],[0,2]]) #行可以不连续，limit做不到

A B C D 2018-01-01 0 1 2 3 2018-01-02 4 5 6 7 2018-01-03 8 9 10 11 2018-01-04 12 13 14 15 2018-01-05 16 17 18 19 2018-01-06 20 21 22 23 A 4 B 5 C 6 D 7 Name: 2018-01-02 00:00:00, dtype: int32 5 2018-01-01 1 2018-01-02 5 2018-01-03 9 2018-01-04 13 2018-01-05 17 2018-01-06 21 Freq: D, Name: B, dtype: int32 B C 2018-01-01 1 2 2018-01-02 5 6 2018-01-03 9 10 A C 2018-01-02 4 6 2018-01-04 12 14 2018-01-06 20 22

# print(df3.ix[:3, ['A', 'C']])\

print(df3);print()

print(df3[df3.A >= 8]) #根据值进行条件过滤，类似where A >= 8条件语句

A B C D 2018-01-01 0 1 2 3 2018-01-02 4 5 6 7 2018-01-03 8 9 10 11 2018-01-04 12 13 14 15 2018-01-05 16 17 18 19 2018-01-06 20 21 22 23 A B C D 2018-01-03 8 9 10 11 2018-01-04 12 13 14 15 2018-01-05 16 17 18 19 2018-01-06 20 21 22 23

indexes1 = pd.date_range('20180101', periods=6)

df4 = pd.DataFrame(np.arange(24).reshape(6, 4), index=indexes1, columns=['A', 'B', 'C', 'D'])

print(df4);print()

#给某个元素赋值

df4.A[1] = 1111

df4.B['20180103'] = 2222

df4.iloc[3, 2] = 3333

df4.loc['20180105', 'D'] = 4444

print(df4);print()

#范围赋值

df4.B[df4.A < 10] = -1

print(df4);print()

df4[df4.A < 10] = 0

print(df4);print()

A B C D 2018-01-01 0 1 2 3 2018-01-02 4 5 6 7 2018-01-03 8 9 10 11 2018-01-04 12 13 14 15 2018-01-05 16 17 18 19 2018-01-06 20 21 22 23 A B C D 2018-01-01 0 1 2 3 2018-01-02 1111 5 6 7 2018-01-03 8 2222 10 11 2018-01-04 12 13 3333 15 2018-01-05 16 17 18 4444 2018-01-06 20 21 22 23 A B C D 2018-01-01 0 -1 2 3 2018-01-02 1111 5 6 7 2018-01-03 8 -1 10 11 2018-01-04 12 13 3333 15 2018-01-05 16 17 18 4444 2018-01-06 20 21 22 23 A B C D 2018-01-01 0 0 0 0 2018-01-02 1111 5 6 7 2018-01-03 0 0 0 0 2018-01-04 12 13 3333 15 2018-01-05 16 17 18 4444 2018-01-06 20 21 22 23

indexes1 = pd.date_range('20180101', periods=6)

df4 = pd.DataFrame(np.arange(24).reshape(6, 4), index=indexes1, columns=['A', 'B', 'C', 'D'])

print(df4);print()

#添加一列

df4['E'] = np.NaN

print(df4);print()

#由于index没对齐，原df没有的行默认为NaN，类型为float64,多出的行丢弃

df4['F'] = pd.Series([1,2,3,4,5,6], index=pd.date_range('20180102', periods=6))

print(df4);print()

print(df4.dtypes)

A B C D 2018-01-01 0 1 2 3 2018-01-02 4 5 6 7 2018-01-03 8 9 10 11 2018-01-04 12 13 14 15 2018-01-05 16 17 18 19 2018-01-06 20 21 22 23 A B C D E 2018-01-01 0 1 2 3 NaN 2018-01-02 4 5 6 7 NaN 2018-01-03 8 9 10 11 NaN 2018-01-04 12 13 14 15 NaN 2018-01-05 16 17 18 19 NaN 2018-01-06 20 21 22 23 NaN A B C D E F 2018-01-01 0 1 2 3 NaN NaN 2018-01-02 4 5 6 7 NaN 1.0 2018-01-03 8 9 10 11 NaN 2.0 2018-01-04 12 13 14 15 NaN 3.0 2018-01-05 16 17 18 19 NaN 4.0 2018-01-06 20 21 22 23 NaN 5.0 A int32 B int32 C int32 D int32 E float64 F float64 dtype: object

df_t = pd.DataFrame(np.arange(24).reshape(6, 4), index=[1,2,3,4,5,6], columns=['A', 'B', 'C', 'D'])

df_t.iloc[0, 1] = np.NaN

df_t.iloc[1, 2] = np.NaN

df = df_t.copy()

print(df);print()

print(df.dropna(axis=0, how='any'));print()

df = df_t.copy()

print(df.dropna(axis=1, how='any'));print()

df = df_t.copy()

df.C = np.NaN

print(df);print()

print(df.dropna(axis=1, how='all'));print()

A B C D 1 0 NaN 2.0 3 2 4 5.0 NaN 7 3 8 9.0 10.0 11 4 12 13.0 14.0 15 5 16 17.0 18.0 19 6 20 21.0 22.0 23 A B C D 3 8 9.0 10.0 11 4 12 13.0 14.0 15 5 16 17.0 18.0 19 6 20 21.0 22.0 23 A D 1 0 3 2 4 7 3 8 11 4 12 15 5 16 19 6 20 23 A B C D 1 0 NaN NaN 3 2 4 5.0 NaN 7 3 8 9.0 NaN 11 4 12 13.0 NaN 15 5 16 17.0 NaN 19 6 20 21.0 NaN 23 A B D 1 0 NaN 3 2 4 5.0 7 3 8 9.0 11 4 12 13.0 15 5 16 17.0 19 6 20 21.0 23

df = df_t.copy()

print(df);print()

print(df.isna());print()

print(df.isnull().any());print() #isnull是isna别名，功能一样

print(df.isnull().any(axis=1));print()

print(np.any(df.isna() == True));print()

print(df.fillna(value=0)) #将NaN赋值

A B C D 1 0 NaN 2.0 3 2 4 5.0 NaN 7 3 8 9.0 10.0 11 4 12 13.0 14.0 15 5 16 17.0 18.0 19 6 20 21.0 22.0 23 A B C D 1 False True False False 2 False False True False 3 False False False False 4 False False False False 5 False False False False 6 False False False False A False B True C True D False dtype: bool 1 True 2 True 3 False 4 False 5 False 6 False dtype: bool True A B C D 1 0 0.0 2.0 3 2 4 5.0 0.0 7 3 8 9.0 10.0 11 4 12 13.0 14.0 15 5 16 17.0 18.0 19 6 20 21.0 22.0 23

data = pd.read_csv('D:/pythonwp/test/student.csv')

print(data)

data.to_pickle('D:/pythonwp/test/student.pickle')

id name age gender 0 1 牛帅 23 Male 1 2 gyb 89 Male 2 3 xxs 27 Male 3 4 hey 24 Female 4 5 奥莱利赫本 66 Female 5 6 Jackson 61 Male 6 7 牛帅 23 Male

df0 = pd.DataFrame(np.ones((3, 4)) * 0, columns=['A', 'B', 'C', 'D'])

df1 = pd.DataFrame(np.ones((3, 4)) * 1, columns=['A', 'B', 'C', 'D'])

df2 = pd.DataFrame(np.ones((3, 4)) * 2, columns=['A', 'B', 'C', 'D'])

print(df0); print()

print(df1); print()

print(df2); print()

res = pd.concat([df0, df1, df2], axis = 0)

print(res); print()

res = pd.concat([df0, df1, df2], axis = 0, ignore_index=True)

print(res)

A B C D 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 A B C D 0 1.0 1.0 1.0 1.0 1 1.0 1.0 1.0 1.0 2 1.0 1.0 1.0 1.0 A B C D 0 2.0 2.0 2.0 2.0 1 2.0 2.0 2.0 2.0 2 2.0 2.0 2.0 2.0 A B C D 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0 1.0 1.0 1.0 1.0 1 1.0 1.0 1.0 1.0 2 1.0 1.0 1.0 1.0 0 2.0 2.0 2.0 2.0 1 2.0 2.0 2.0 2.0 2 2.0 2.0 2.0 2.0 A B C D 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 5 1.0 1.0 1.0 1.0 6 2.0 2.0 2.0 2.0 7 2.0 2.0 2.0 2.0 8 2.0 2.0 2.0 2.0

df0 = pd.DataFrame(np.ones((3, 4)) * 0, columns=['A', 'B', 'C', 'D'])

df1 = pd.DataFrame(np.ones((3, 4)) * 1, columns=['E', 'F', 'C', 'D'])

res = pd.concat([df0, df1], ignore_index=True)

print(res);print()

res = pd.concat([df0, df1], join='outer', ignore_index=True)

print(res);print()

res = pd.concat([df0, df1], join='inner',ignore_index=True)

print(res);print()

A B C D E F 0 0.0 0.0 0.0 0.0 NaN NaN 1 0.0 0.0 0.0 0.0 NaN NaN 2 0.0 0.0 0.0 0.0 NaN NaN 3 NaN NaN 1.0 1.0 1.0 1.0 4 NaN NaN 1.0 1.0 1.0 1.0 5 NaN NaN 1.0 1.0 1.0 1.0 A B C D E F 0 0.0 0.0 0.0 0.0 NaN NaN 1 0.0 0.0 0.0 0.0 NaN NaN 2 0.0 0.0 0.0 0.0 NaN NaN 3 NaN NaN 1.0 1.0 1.0 1.0 4 NaN NaN 1.0 1.0 1.0 1.0 5 NaN NaN 1.0 1.0 1.0 1.0 C D 0 0.0 0.0 1 0.0 0.0 2 0.0 0.0 3 1.0 1.0 4 1.0 1.0 5 1.0 1.0

#横向合并

df0 = pd.DataFrame(np.ones((3, 4)) * 0, index=['1', '2', '3'], columns=['A', 'B', 'C', 'D'])

df1 = pd.DataFrame(np.ones((3, 4)) * 1, index=['2', '3', '4'], columns=['A', 'B', 'C', 'D'])

print(df0);print()

print(df1);print()

res = pd.concat([df0, df1], axis=1)

print(res);print()

res = pd.concat([df0, df1], axis=1, join='inner', ignore_index=True)

print(res);print()

res = pd.concat([df0, df1], axis=1, join_axes=[df0.index])

print(res);print()

A B C D 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 A B C D 2 1.0 1.0 1.0 1.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 A B C D A B C D 1 0.0 0.0 0.0 0.0 NaN NaN NaN NaN 2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 4 NaN NaN NaN NaN 1.0 1.0 1.0 1.0 0 1 2 3 4 5 6 7 2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 A B C D A B C D 1 0.0 0.0 0.0 0.0 NaN NaN NaN NaN 2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0

df0 = pd.DataFrame(np.ones((3, 4)) * 0, index=['1', '2', '3'], columns=['A', 'B', 'C', 'D'])

df1 = pd.DataFrame(np.ones((3, 4)) * 1, index=['2', '3', '4'], columns=['A', 'B', 'C', 'D'])

print(df0);print()

print(df1);print()

res = df0.append([df1, df1], ignore_index=False)

print(res);print()

s = pd.Series([1,2,3,4], index=['A','B','C','E'])

print(df0.append(s, ignore_index=True))

A B C D 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 A B C D 2 1.0 1.0 1.0 1.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 A B C D 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 2 1.0 1.0 1.0 1.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 2 1.0 1.0 1.0 1.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 A B C D E 0 0.0 0.0 0.0 0.0 NaN 1 0.0 0.0 0.0 0.0 NaN 2 0.0 0.0 0.0 0.0 NaN 3 1.0 2.0 3.0 NaN 4.0

df1 = pd.DataFrame({'key':['K0', 'K1', 'K2'],

'A':['A0', 'A1', 'A2'],

'B':['B0', 'B1', 'B2']})

df2 = pd.DataFrame({'key':['K3', 'K1', 'K2'],

'C':['C3', 'C1', 'C2'],

'D':['D3', 'D1', 'D2']})

print(df1); print()

print(df2); print()

res = pd.merge(df1, df2, on='key')

print(res); print()

res = pd.merge(df1, df2, on='key', how='outer')

print(res); print()

res = pd.merge(df1, df2, on='key', how='left')

print(res); print()

res = pd.merge(df1, df2, on='key', how='right')

print(res); print()

A B key 0 A0 B0 K0 1 A1 B1 K1 2 A2 B2 K2 C D key 0 C3 D3 K3 1 C1 D1 K1 2 C2 D2 K2 A B key C D 0 A1 B1 K1 C1 D1 1 A2 B2 K2 C2 D2 A B key C D 0 A0 B0 K0 NaN NaN 1 A1 B1 K1 C1 D1 2 A2 B2 K2 C2 D2 3 NaN NaN K3 C3 D3 A B key C D 0 A0 B0 K0 NaN NaN 1 A1 B1 K1 C1 D1 2 A2 B2 K2 C2 D2 A B key C D 0 A1 B1 K1 C1 D1 1 A2 B2 K2 C2 D2 2 NaN NaN K3 C3 D3

df1 = pd.DataFrame({'key1':['K0', 'K0', 'K1'],

'key2':['K0', 'K1', 'K1'],

'A':['A0', 'A1', 'A2'],

'B':['B0', 'B1', 'B2']})

df2 = pd.DataFrame({'key1':['K0', 'K0', 'K1', 'K2'],

'key2':['K0', 'K0', 'K1', 'K2'],

'C':['C3', 'C1', 'C2', 'C4'],

'D':['D3', 'D1', 'D2', 'D4']})

print(df1); print()

print(df2); print()

res = pd.merge(df1, df2, on=['key1','key2'])

print(res); print()

res = pd.merge(df1, df2, on=['key1','key2'], how='outer', indicator='indi')

print(res); print()

A B key1 key2 0 A0 B0 K0 K0 1 A1 B1 K0 K1 2 A2 B2 K1 K1 C D key1 key2 0 C3 D3 K0 K0 1 C1 D1 K0 K0 2 C2 D2 K1 K1 3 C4 D4 K2 K2 A B key1 key2 C D 0 A0 B0 K0 K0 C3 D3 1 A0 B0 K0 K0 C1 D1 2 A2 B2 K1 K1 C2 D2 A B key1 key2 C D indi 0 A0 B0 K0 K0 C3 D3 both 1 A0 B0 K0 K0 C1 D1 both 2 A1 B1 K0 K1 NaN NaN left_only 3 A2 B2 K1 K1 C2 D2 both 4 NaN NaN K2 K2 C4 D4 right_only

#以上是根据值合并。下面根据index合并

df1 = pd.DataFrame({'A':['A0', 'A1', 'A2'],

'B':['B0', 'B1', 'B2']},

index=['index0', 'index1', 'index2'])

df2 = pd.DataFrame({'A':['C3', 'C1', 'C2'],

'D':['D3', 'D1', 'D2']},

index=['index3', 'index1', 'index2'])

print(df1); print()

print(df2); print()

res = pd.merge(df1, df2, left_index=True, right_index=True)

print(res); print()

res = pd.merge(df1, df2, left_index=True, right_index=True, how='outer', suffixes=['_b', '_g'])

print(res); print()

A B index0 A0 B0 index1 A1 B1 index2 A2 B2 A D index3 C3 D3 index1 C1 D1 index2 C2 D2 A_x B A_y D index1 A1 B1 C1 D1 index2 A2 B2 C2 D2 A_b B A_g D index0 A0 B0 NaN NaN index1 A1 B1 C1 D1 index2 A2 B2 C2 D2 index3 NaN NaN C3 D3

res = df1.join(df2, how='outer', lsuffix='_left', rsuffix='_right') #不用on默认用索引合并

print(res);print()

res = df1.join(df2, on='B', how='outer', lsuffix='_left', rsuffix='_right') #用on指定df1的某列和df2的索引合并

print(res);print()

A_left B A_right D index0 A0 B0 NaN NaN index1 A1 B1 C1 D1 index2 A2 B2 C2 D2 index3 NaN NaN C3 D3 A_left B A_right D index0 A0 B0 NaN NaN index1 A1 B1 NaN NaN index2 A2 B2 NaN NaN index2 NaN index3 C3 D3 index2 NaN index1 C1 D1 index2 NaN index2 C2 D2

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt #画图模块

s = pd.Series(np.random.randn(1000), index=np.arange(1000))

s = s.cumsum()

#须在命令行执行， jupyter会报错

#s.plot()

#plt.show()

df = pd.DataFrame(np.random.randn(1000, 3), columns=['A', 'B', 'C'])

df = df.cumsum()

print(df.head()); print() #head默认显示前5行

#须在命令行执行， jupyter会报错

#s.plot()

#plt.show()

#须在命令行执行， jupyter会报错

#'bar', 'hist', 'box', 'kde', 'area', 'scatter', 'hexbin', 'pie'...

#class_B = df.plot.scatter(x='A', y='B', color='DarkBlue', label='Class B') #画图，scatter

#df.plot.scatter(x='A', y='C', color='DarkRed', label='Class C', class_B=class_B)

#plt.show()

A B C 0 -0.399363 -1.004210 0.641141 1 -1.970009 -0.608482 -0.758504 2 -3.081640 -0.617352 -1.143872 3 -2.174627 -1.383785 -1.011411 4 -1.415515 -1.892226 -2.511739

Python常见的pandas用法demo示例

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